A comparison of discriminant procedures for binary variables
Computational Statistics & Data Analysis
Rapid and brief communication: Classification of run-length encoded binary data
Pattern Recognition
Classification-based collaborative filtering using market basket data
Expert Systems with Applications: An International Journal
Texture classification via conditional histograms
Pattern Recognition Letters
| Hi-index | 12.05 |
A new method of discriminant analysis of classifying binary data is proposed by considering an exact joint probability mass function of correlated binary variables. The interaction order of the joint probability mass function can be controlled for the performance of the proposed method on the classification accuracy and computational time. The performance in terms of the misclassification rate for a real data and some artificial data sets was reported and compared with those of linear discriminant analysis and logistic regression.